AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Sequence Analysis, RNA

Showing 111 to 120 of 294 articles

Clear Filters

EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, s...

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

IEEE/ACM transactions on computational biology and bioinformatics
Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classificatio...

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

Genes
OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) ...

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

Nature communications
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even...

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Aging
Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we s...

RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.

Functional & integrative genomics
Adenosine to inosine (A-to-I) editing in RNA is involved in various biological processes like gene expression, alternative splicing, and mRNA degradation associated with carcinogenesis and various human diseases. Therefore, accurate identification of...

A network of core and subtype-specific gene expression programs in myositis.

Acta neuropathologica
Myositis comprises a heterogeneous group of skeletal muscle disorders which converge on chronic muscle inflammation and weakness. Our understanding of myositis pathogenesis is limited, and many myositis patients lack effective therapies. Using muscle...

SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells.

Genome biology
Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq....

bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network.

Genes
RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction ...